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Today, companies are struggling to develop their human resources analytics (HRA) capability, although interest in the subject is rapidly increasing. Furthermore, the academic literature on the subject is immature with limited practical guidance or comprehensive models that could support organisations in the development of their HRA capability. To address this issue, the aim of this paper is to provide a maturity model – i.e. HRAMM – and an interdependency matrix through which an organisation can (1) operationalise its HRA capability and assess its organisational maturity; (2) generate harmonious development roadmaps to improve its HRA capability; and (3) enable benchmarking and continuous improvement.

The research described in this paper is based on the popular methodology proposed by Becker et al. (2009) and the procedure for maturity evaluation developed by Gastaldi et al. (2018). This method combines academic rigour and field experience in analytics, in a process spanning eight main phases that involves literature reviews and knowledge creation techniques.

We define HRA maturity through four areas and 14 dimensions, providing a comprehensive model to operationalise HRA capability. Additionally, we argue that HRA maturity develops through an evolutionary path described in four discrete stages of maturity that go beyond traditional analytics sophistication. Lastly, the interdependency matrix reveals specific enablers for the development of HRA.

This paper provides practitioners with useful tools to monitor, evaluate and plan their HRA development path. Additionally, our research helps practitioners to prioritise their work and investment, generating an effective roadmap for developing and improving their HRA capability.

To the best of the authors’ knowledge, this study is the first to provide a model for evaluating the maturity of HRA capability plus an interdependency matrix to evaluate systematically the prerequisites and synergies among its constituting dimensions.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The primary objective of this chapter is to synthesize and organize prevailing theoretical perspectives on metacognition into a framework that can enhance understanding of metacognitive phenomena, with the aim of stimulating future research in the field of organizational behavior and human resources management (OBHRM). The author starts with a review of the history of metacognition research, distinguishing it from related theoretical constructs such as cognition, executive function, and self-regulation. Following this, the author outlines five constituent elements of metacognition – metacognitive knowledge, metacognitive experiences, metacognitive monitoring, a dynamic mental model, and metacognitive control – with discussions on their interrelationships and respective functions. Two approaches to metacognition, a process approach and an individual-difference approach, are then presented, summarizing key questions and findings from each. Finally, three broad directions for future research in OBHRM are proposed: examining metacognitive processes, considering mechanisms beyond learning to explain the effects of metacognition, and exploring both domain-specific and general metacognitive knowledge and skills. The implications of these research directions for personnel and human resources management practices are discussed.

The current research elucidates the role of empathy in design of artificial intelligence (AI) systems in healthcare context, through a structured literature review, analysis and synthesis of academic literature published between 1990 and 2024.

This study aims to advance the domain of empathy in AI by adopting theory constructs context method approach using the PRISMA 2020 framework.

The study presents a current state-of-the-art literature to review the connections between empathy and AI and identifying four clusters showing the emerging trajectories in the field of AI and empathy in healthcare setting.

Despite a rise in empirical research, the potential pathways enhancing AI accountability by incorporation of empathy is unclear. The research aims to contribute to the existing literature on AI and empathy in the healthcare sector by carving out four distinct clusters depicting the future research avenues.

Artificial intelligence (AI) technologies have led to significant transformations across industries and society, including the field of education. The integration of AI in educational settings has the potential to improve students' learning experience and support their individual competencies when paired with non-AI methods. Despite the growing importance of AI in modern education, there remains a noticeable research gap regarding its use in entrepreneurship education and the effects of Chatbots on students' entrepreneurial competencies. To address this gap, an exploratory study was conducted on undergraduate students who were tasked with using ChatGPT to improve their business model canvas.

The chosen methodology aligned with the research purpose, aiming to explore the relationship between Generative AI and competencies. Due to the novel nature of the research problem, an exploratory study was conducted using a mixed methods approach. A survey with open- and closed-ended questions was designed, and statistical and text analyses were performed to interpret data and test identified propositions.

The findings of this study indicate that ChatGPT can enhance the types of students' entrepreneurial competencies considered in this study: spotting opportunities, creativity, vision, valuing ideas and ethical and sustainable thinking. The results show that ChatGPT can be particularly helpful to improve the ability of students of valuing ideas.

Overall, this study highlights the potential of adopting ChatGPT in experiential learning methodologies for enhancing students' entrepreneurial competencies and improving their learning outcomes.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.

This paper tries to retrospect the mounting application of machine learning (ML) and artificial intelligence (AI) in the human resource management area. The document applies bibliometric analysis and uses relational techniques to explore dimensions of documents in the field. The results highlight publication trends, most impactful authors, countries and institutes in the research area. The science mapping along with co-citation and bibliometric coupling analysis revealed major developments in the field. The thematic mapping and trend analysis highlighted the past and emerging trends towards significant and impactful research in the areas of robotics, big data, AI and data analytics. This paper sets the base for future researchers by coordinating and combining various past researches to help in understanding the evolution of ML and AI in human resource management and expansion of knowledgebase.

This paper aims to explore the role of blockchain (BCT) and Artificial Intelligence (AI) technologies in enhancing and incentivizing environmentally responsible, socially inclusive and economically viable tourism practices within the hospitality and tourism (H&T) industry.

This study is based on a critical reflection research approach that enables a synthesis of information derived from existing literature's insights and the authors' experiences and observations. By examining frameworks and theories in the literature, critical reflection also helps develop a more comprehensive understanding of the topic.

This study portrays how BCT could be used to track the sustainability credentials of tourism providers and how AI can optimize energy usage in hotels. This study depicts how adopting technology-driven sustainable practices in the H&T industry can increase profitability, improve reputation, compliance with regulations, efficiency and a better guest experience.

The study suggests targeted actions and policy frameworks that can be tailored to different stakeholder groups (hospitality businesses, policymakers and tourists) to overcome barriers and maximize the positive societal and environmental impacts of adopting BCT and AI for sustainable tourism.

The originality of this study lies in its ability to offer new perspectives and novel recommendations on the diverse uses of AI and BCT in the context of sustainable tourism. Furthermore, the study provides strategic and policy elements (Targeted actions and policy frameworks) for stakeholders to integrate sustainable tourism practices using BCT and AI successfully. This study differs from earlier review studies that primarily focused on adopting emerging technologies and ignoring the sustainability angle in the use of technology.

The purpose of this paper is to supplement and update previously published articles about artificial intelligence (AI) instruments and operations in banking sectors with the following objectives in mind: to understand the role of AI in banking sectors; to explore the themes and context in this area based on keywords, co-citations and co-words; and to identify future research direction by evaluating the trend and direction of previous research.

This study adopts a semi-inductive approach with the convolution of bibliometrics and literature review. This study used bibliometrics for the identification of literature across multiple databases and systematic literature review on identified articles to explore heterogeneous sectors within AI in banking and finance.

This study contributes a literature-based model that accounts for both the broadly in AI application in banking and finance: predictive modeling in risk assessment and detection; financial decision-making; client service delivery; and emerging FinTech applications of AI and machine learning.

This study is among the few to address the literature of tools and application of AI in banking through mixed-methods approach and produce a synthesized model for the same.

The effect of digital transition on knowledge management (KM) processes is becoming relevant for companies operating in different industries and the body of literature examining this impact is rapidly growing. This paper aims to critically analyse the literature on the impact of digital transition on KM by rethinking the SECI model proposed by Nonaka and proposing the WISED model for the digital knowledge-creating company.

The systematisation of existing studies on the topic and the analysis of the evolution of knowledge creation process in the era of digital transition was carried out through a bibliometric approach.

According to the traditional epistemological and ontological dimensions and considering the innovative KM processes identified by this study (i.e. webification, informalisation, systematisation, explicitation and digitalisation), the results highlight how the proposed WISED model can be adopted by organizations to manage knowledge through the use of digital technologies.

Digital transition seems to open up new horizons that can expand the potential use of the WISED model for organisations and society.